cmap=cm.coolwarm, title=pols[m], xlabel='Frequency (Mhz)',
ylabel='Obs', cbar_label='Fraction', xticks=xticks, xminors=xminors,
yminors=yminors, xticklabels=xticklabels, zero_mask=False,
mask_color='black')
pl.image_plot(fig_INS_s, ax_INS_s[m / 2][m % 2], INS_stack[:, 0, :, m],
title=pols[m], cbar_label='Amplitude (UNCALIB)', xticks=xticks,
aspect_ratio=0.1, xminors=xminors, xticklabels=xticklabels,
zero_mask=False)
pl.image_plot(fig_FD_s, ax_FD_s[m / 2][m % 2], FD_stack[:, 0, :, m],
cmap=cm.coolwarm, title=pols[m], cbar_label='Fraction',
aspect_ratio=0.1, xticks=xticks, xminors=xminors,
xticklabels=xticklabels, zero_mask=False, mask_color='black')
for m in range(2):
pl.line_plot(fig_m, ax_m[m], [[INS_m[0, :, n] for n in range(4)],
[frac_diff_m[0, :, n] for n in range(4)]][m],
title=avg_title[m], xlabel='Frequency (Mhz)', ylabel=ylabels[m],
labels=pols, xticks=xticks, xticklabels=xticklabels,
xminors=xminors)
pl.line_plot(fig_mp, ax_mp[m], [[INS_m[0].mean(axis=-1), ], [frac_diff_m[0].mean(axis=-1), ]][m],
title=avg_title[m], xlabel='Frequency (Mhz)', ylabel=ylabels[m],
xticks=xticks, xticklabels=xticklabels, xminors=xminors,
legend=False)
fig_INS.savefig('%sGolden_Set_INS.png' % (outpath))
fig_fd.savefig('%sGolden_Set_FD.png' % (outpath))
fig_m.savefig('%sGolden_Set_INS_FD_tavg.png' % (outpath))
fig_mp.savefig('%sGolden_Set_INS_FD_tpavg.png' % (outpath))
fig_INS_s.savefig('%sGolden_Set_INS_noavg.png' % (outpath))
fig_FD_s.savefig('%sGolden_Set_FD_noavg.png' % (outpath))
fig.savefig('%s%i/%s_match_filter_MS.png' % (outpath, sig_thresh, obs))
plt.close(fig)
occ = occ_num / occ_den * 100
occ_freq = (occ_freq_num.transpose() / occ_freq_den).transpose() * 100
np.save('%sRFI_occupancy_sigma.npy' % (outpath), occ)
np.save('%sRFI_occupancy_freq.npy' % (outpath), occ_freq)
fig, ax = plt.subplots(figsize=(14, 8))
fig_f, ax_f = plt.subplots(figsize=(14, 8))
xticks = range(-5, 25, 5)
xticklabels = [str(tick + 4) for tick in xticks]
pl.line_plot(fig,
ax, [
occ,
],
title='RFI Occupancy at Different Thresholds',
xlabel='Sigma',
ylabel='Percent Occupancy',
xticks=xticks,
xticklabels=xticklabels,
legend=False)
xticks = [64 * k for k in range(6)]
xminors = AutoMinorLocator(4)
xticklabels = ['%.1f' % (freq_array[0, tick] * 10**(-6)) for tick in xticks]
pl.line_plot(fig_f,
ax_f,
occ_freq,
title='RFI Occupancy at Different Frequencies',
xlabel='Frequency (Mhz)',
ylabel='Percent Occupancy',
xticks=xticks,
xminors=xminors,
fig, ax = plt.subplots(figsize=(14, 8))
pl.error_plot(fig,
ax,
x,
mu_sim,
None,
s_sim,
label='Simulation',
drawstyle='steps-mid')
pl.error_plot(fig, ax, x, m, None, s, label='Theory', drawstyle='steps-mid')
pl.error_plot(fig,
ax,
fx,
mf,
None,
None,
xlabel='$\sigma$',
ylabel='Counts',
label='Standard Normal PDF')
fig.savefig('%s/Error_Plots.png' % (outpath))
plt.close(fig)
fig, ax = plt.subplots(figsize=(14, 8))
pl.line_plot(fig,
ax, [s, s_sim],
title='Standard Deviation Comparison',
labels=['Theory', 'Simulations'],
xlabel='$\sigma$',
ylabel='STD (Counts)')
fig.savefig('%s/STD_Comp.png' % (outpath))
# anal_sigs = np.random.multivariate_normal([0, 0], cov, size=10**args.s[0])
s = np.random.normal(size=10**args.s[0])
n1 = np.random.normal(size=10**args.s[0])
n2 = np.random.normal(size=10**args.s[0])
anal_sig_1 = combo[0] * (a * s + b * n1)
anal_sig_2 = combo[1] * (a * s + b * n2)
dig_sig_1 = digital_signal(args.n[0], anal_sig_1, args.q[0])
dig_sig_2 = digital_signal(args.n[0], anal_sig_2, args.q[0])
corr[i] = np.mean(dig_sig_1.sig * dig_sig_2.sig)
if not i:
k = rhos[i] / corr[i]
rho_quant[i] = k * corr[i]
frac[i] = rho_quant[i] / rhos[i] - 1
data = [corr, rho_quant, frac]
for axis, y, ylabel in zip(ax, data, ylabels):
plot_args = (fig, axis, rhos, [
y,
])
plot_kwargs.update({'ylabel': ylabel})
pl.line_plot(*plot_args, **plot_kwargs)
if not os.path.exists(args.outpath[0]):
os.makedirs(args.outpath[0])
fig.savefig('%s/digital_correlation_sim_n%i_q%i_s%i.png' %
(args.outpath[0], args.n[0], args.q[0], args.s[0]))
print('Ended at %s' % (time.strftime('%H:%M:%S')))
plot_lib.image_plot(fig_ratio,
ax_ratio[m / 2][m % 2],
ratio[:, :, m],
title=pol_titles[m],
cbar_label='Excess/Average',
xticks=xticks,
xticklabels=xticklabels,
xminors=xminors,
zero_mask=False,
cmap=cm.coolwarm)
plot_lib.line_plot(
fig_line,
ax_line[m / 2][m % 2], [
mean[:, m], fit[:, m], fit[:, m] + fit_centers[:, m],
fit[:, m] + fit_edges[:, m]
],
title=pol_titles[m],
xticks=xticks,
xminors=xminors,
xticklabels=xticklabels,
zorder=[1, 2, 2, 2],
labels=['Template', 'Fit', 'Center Teeth Fit', 'Edge Teeth Fit'])
plot_lib.scatter_plot_2d(fig_scatter,
ax_scatter[m / 2][m % 2],
temps[:, m],
mean[:, m],
title=pol_titles[m],
xlabel='Fit Width',
ylabel='Template')
fig_exc.savefig('%s%s_Vis_Avg_Excess.png' % (plot_dir, obslist[n]))
fig_ratio.savefig('%s%s_Vis_Avg_Ratio.png' % (plot_dir, obslist[n]))